Current ANNs, including deep-learning frameworks, are what computational neuroscientists refer to as "rate-encoding models", in that the real-value of the output from a unit (roughly approximating an integrating neuron) represents a firing rate, or a probability of firing, over time. These networks accept inputs all at once and are also referred to as "linear non-linear" models ([1]}), in that they represent a combination of a linear operation (summation of incoming connections), as well as a non-linear operation called the "activation function" (some sort of sigmoidal, inverse tangent function, or rectified linear unit). The rate \(R\)  of a neuron's fire is algebraically:
\(R= N / T\) 
